Diagnosing urban traffic anomalies by integrating geographic knowledge and tensor theory

Diagnosing urban traffic anomalies by integrating geographic knowledge and tensor theory

Urban traffic anomaly diagnosis is crucial for urban road management and smart city construction. Most existing methods perform anomaly detection from a data-driven perspective and ignore the unique spatiotemporal characteristics of traffic anomalies, resulting in reduced accuracy or incorrect extra...

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Bibliographic Details
Main Authors: Zilong Zhao, Luliang Tang, Chang Ren, Xue Yang, Zihan Kan, Qingquan Li
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:GIScience & Remote Sensing
Subjects:
Trajectory data
geographic knowledge
crowdsourced geographic information
low-rank sparse tensor
spatiotemporal correlation
urban anomaly diagnosis
Online Access:https://www.tandfonline.com/doi/10.1080/15481603.2023.2290347
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https://www.tandfonline.com/doi/10.1080/15481603.2023.2290347

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